Korean Dialect Identification Based on an Ensemble of Prosodic and Segmental Feature Learning for Forensic Speaker Profiling

Lee, Jooyoung; Kim, Kyungwha; Chung, Minhwa

doi:10.1109/o-cocosda202257103.2022.9997971

2022 25th Conference of the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databa 2022

DOI: 10.1109/o-cocosda202257103.2022.9997971

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Korean Dialect Identification Based on an Ensemble of Prosodic and Segmental Feature Learning for Forensic Speaker Profiling

Jooyoung Lee

Kyungwha Kim

Minhwa Chung

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2023

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“…특성을 사용한 딥러닝 기반의 방법이 주류를 이루었다 (Chowdhury et al, 2020;Khurana et al, 2017;Mukherjee et al, 2020;Tawaqal & Suyanto, 2021;Wan et al, 2022;Wang et al, 2021;Zhang & Hansen, 2018). 최근에는 MFCC 특성에 운율학적 (Bhattacharjee & Sarmah, 2013;Lee et al, 2021Lee et al, , 2022Mehrabani & Hansen, 2015), 음성학적 (Chittaragi & Koolagudi, 2019;Najafian et al, 2018) 혹은 어휘적 (Michon et al, 2018;Ying et al, 2020) 특성을 결합하여 방언 분류 모델을 훈련시키는 연구가 많이 소개되고 있다. 최근의 연구에서는…”

unclassified

Dialect classification based on the speed and the pause of speech utterances*

Junyeong¹,

Lee²

2023

Phonetics Speech Sci.

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In this paper, we propose an approach for dialect classification based on the speed and pause of speech utterances as well as the age and gender of the speakers. Dialect classification is one of the important techniques for speech analysis. For example, an accurate dialect classification model can potentially improve the performance of speaker or speech recognition. According to previous studies, research based on deep learning using Mel-Frequency Cepstral Coefficients (MFCC) features has been the dominant approach. We focus on the acoustic differences between regions and conduct dialect classification based on the extracted features derived from the differences. In this paper, we propose an approach of extracting underexplored additional features, namely the speed and the pauses of speech utterances along with the metadata including the age and the gender of the speakers. Experimental results show that our proposed approach results in higher accuracy, especially with the speech rate feature, compared to the method only using the MFCC features. The accuracy improved from 91.02% to 97.02% compared to the previous method that only used MFCC features, by incorporating all the proposed features in this paper.

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unclassified

Dialect classification based on the speed and the pause of speech utterances*

Junyeong¹,

Lee²

2023

Phonetics Speech Sci.

View full text Add to dashboard Cite

show abstract

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Korean Dialect Identification Based on an Ensemble of Prosodic and Segmental Feature Learning for Forensic Speaker Profiling

Cited by 1 publication

References 10 publications

Dialect classification based on the speed and the pause of speech utterances*

Dialect classification based on the speed and the pause of speech utterances*

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